'''simple docstring'''




# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class        a__ (  UpperCAmelCase__				,  UpperCAmelCase__					):
     lowerCamelCase    :	List[Any]				=1






     @register_to_config
     def __init__(					self						:						List[Any]    ,							a						:						int=20_00    ,							a						:						Optional[Any]=0.1    ,							a						:						Optional[int]=20    ,							a						:						List[str]=1e-3       ):





         """simple docstring"""



         __lowerCamelCase            =    None
         __lowerCamelCase            =    None
         __lowerCamelCase            =    None






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						Tuple    ,							a						:						Union[str, torch.device] = None       ):





         """simple docstring"""



         __lowerCamelCase            =    torch.linspace(1    ,							self.config.sampling_eps    ,							a    ,							device=a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[Any]    ,							a						:						int    ,							a						:						Optional[Any]    ,							a						:						List[Any]=None       ):





         """simple docstring"""



         if self.timesteps is None:
             raise ValueError(
                 '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''       )

         # TODO(Patrick) better comments + non-PyTorch
         # postprocess model score
         __lowerCamelCase            =    (
             -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
         )
         __lowerCamelCase            =    torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff       )       )
         __lowerCamelCase            =    std.flatten()
         while len(std.shape       ) < len(score.shape       ):
             __lowerCamelCase            =    std.unsqueeze(-1       )
         __lowerCamelCase            =    -score / std

         # compute
         __lowerCamelCase            =    -1.0 / len(self.timesteps       )

         __lowerCamelCase            =    self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
         __lowerCamelCase            =    beta_t.flatten()
         while len(beta_t.shape       ) < len(x.shape       ):
             __lowerCamelCase            =    beta_t.unsqueeze(-1       )
         __lowerCamelCase            =    -0.5 * beta_t * x

         __lowerCamelCase            =    torch.sqrt(a       )
         __lowerCamelCase            =    drift - diffusion**2 * score
         __lowerCamelCase            =    x + drift * dt

         # add noise
         __lowerCamelCase            =    randn_tensor(x.shape    ,							layout=x.layout    ,							generator=a    ,							device=x.device    ,							dtype=x.dtype       )
         __lowerCamelCase            =    x_mean + diffusion * math.sqrt(-dt       ) * noise

         return x, x_mean






     def __len__(					self						:						Tuple       ):





         """simple docstring"""



         return self.config.num_train_timesteps

'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class        a__ :






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.get_dummy_input()






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         if self.block_type == "down":
             return (4, 32, 16, 16)
         elif self.block_type == "mid":
             return (4, 32, 32, 32)
         elif self.block_type == "up":
             return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[Any]=True    ,							a						:						Dict=False    ,							a						:						Optional[Any]=False    ,							a						:						List[Any]=False    ,							):





         """simple docstring"""



         __lowerCamelCase            =    4
         __lowerCamelCase            =    32
         __lowerCamelCase            =    (32, 32)

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    (batch_size, num_channels) + sizes
         __lowerCamelCase            =    randn_tensor(a    ,							generator=a    ,							device=a       )
         __lowerCamelCase            =    {'''hidden_states''': hidden_states}

         if include_temb:
             __lowerCamelCase            =    1_28
             __lowerCamelCase            =    randn_tensor((batch_size, temb_channels)    ,							generator=a    ,							device=a       )

         if include_res_hidden_states_tuple:
             __lowerCamelCase            =    torch.manual_seed(1       )
             __lowerCamelCase            =    (randn_tensor(a    ,							generator=a    ,							device=a       ),)

         if include_encoder_hidden_states:
             __lowerCamelCase            =    floats_tensor((batch_size, 32, 32)       ).to(a       )

         if include_skip_sample:
             __lowerCamelCase            =    randn_tensor(((batch_size, 3) + sizes)    ,							generator=a    ,							device=a       )

         return dummy_input






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    {
             '''in_channels''': 32,
             '''out_channels''': 32,
             '''temb_channels''': 1_28,
         }
         if self.block_type == "up":
             __lowerCamelCase            =    32

         if self.block_type == "mid":
             init_dict.pop('''out_channels'''       )

         __lowerCamelCase            =    self.dummy_input
         return init_dict, inputs_dict






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         unet_block.to(a       )
         unet_block.eval()

         with torch.no_grad():
             __lowerCamelCase            =    unet_block(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         self.assertEqual(output.shape    ,							self.output_shape       )

         __lowerCamelCase            =    output[0, -1, -3:, -3:]
         __lowerCamelCase            =    torch.tensor(a       ).to(a       )
         assert torch_all_close(output_slice.flatten()    ,							a    ,							atol=5e-3       )






     @unittest.skipIf(torch_device == '''mps'''    ,							'''Training is not supported in mps'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         model.to(a       )
         model.train()
         __lowerCamelCase            =    model(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    randn_tensor(output.shape    ,							device=a       )
         __lowerCamelCase            =    torch.nn.functional.mse_loss(a    ,							a       )
         loss.backward()
